Effectiveness of Environmental Education through Social Media in India
Bibliographic record
Abstract
As concerns of climate change rise, the need for effective educational interventions to promote eco-consciousness and sustainable practices among the youth in India has become increasingly urgent. Various studies have explored the effectiveness of environmental education through social media in India. Ahmad (2015) found that videos were more effective than pamphlets in influencing youth on environmental education. However, Chung (2020) noted that while young adults access environmental information through social media, they often do not engage with or share this content. Gupta (2020) emphasized the importance of behavior change theories and social support in enhancing the effectiveness of social media interventions. Laiphrakpam (2019) highlighted the role of environmental education in increasing awareness and driving behavioral change among students in India. These studies collectively suggest that while social media can be a powerful tool for environmental education, its effectiveness depends on the content and engagement strategies used. This study investigates the role of social media as a platform for environmental education and its influence on young people's environmental awareness and behavioral habits. A quantitative research design was employed, utilizing a structured online questionnaire with close-ended questions to collect data from 100 young social media users in India. The survey covered demographics, social media usage, environmental awareness and behavioral habits. The results demonstrate that social media can effectively increase environmental awareness among young people and encourage healthy behavioral changes. This study underscores the potential of social media as an effective instrument for spreading environmental education and fostering a generation of environmentally responsible citizens in India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".